Blind Stereoscopic Image Quality Assessment Accounting for Human Monocular Visual Properties and Binocular Interactions

Human visual perceptual model is a key factor for evaluating stereoscopic image quality. This paper focuses on the contributions of monocular and binocular properties on quality perception and proposes a novel blind stereoscopic image quality assessment model by comprehensively digging the relations...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.33666-33678
Main Authors: Liu, Yun, Yan, Weiqing, Zheng, Zhi, Huang, Baoqing, Yu, Hongwei
Format: Article
Language:English
Subjects:
binocular feature
Distortion
Feature extraction
human visual system
Image quality
monocular feature
Perception
Quality assessment
Regression models
Solid modeling
Statistical analysis
Stereo image processing
Stereoscopic image quality
Stereoscopy
Support vector machines
Three-dimensional displays
Visualization
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Summary:Human visual perceptual model is a key factor for evaluating stereoscopic image quality. This paper focuses on the contributions of monocular and binocular properties on quality perception and proposes a novel blind stereoscopic image quality assessment model by comprehensively digging the relationship between visual features and quality perception. The statistical quality-aware monocular features are extracted from both left view and right view to reveal monocular quality perception, including the color statistical features which are missed in most previous models, while the multiple features of the summation signal and the entropy features of the difference signal are extracted to quantify the binocular quality perception. Finally, support vector regression (SVR) is utilized to train a regression model based on the extracted features and the subjective scores. Three public databases, LIVE 3D Phase I, LIVE 3D Phase II, and MCL 3D Database, are adopted to prove the effectiveness of the proposed model. Experimental results demonstrate that the proposed model is superior to other existing state-of-the-art quality metrics.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2974006